Merge pull request #16 from FawadAhmed322/triton_example

Add python example of sending request to model deployed on Triton

examples/clearml_serving_simple_http_inference_request/client.py

@@ -0,0 +1,88 @@
+#!/usr/bin/env python
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions
+# are met:
+#  * Redistributions of source code must retain the above copyright
+#    notice, this list of conditions and the following disclaimer.
+#  * Redistributions in binary form must reproduce the above copyright
+#    notice, this list of conditions and the following disclaimer in the
+#    documentation and/or other materials provided with the distribution.
+#  * Neither the name of NVIDIA CORPORATION nor the names of its
+#    contributors may be used to endorse or promote products derived
+#    from this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
+# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+# PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
+# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
+# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+import argparse
+from PIL import Image
+import numpy as np
+
+from http_triton import InferenceServerClient, InferInput
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-v',
+                        '--verbose',
+                        action="store_true",
+                        required=False,
+                        default=False,
+                        help='Enable verbose output')
+    parser.add_argument('-u',
+                        '--url',
+                        type=str,
+                        required=False,
+                        default='localhost:8000',
+                        help='Inference server URL. Default localhost:8000')
+
+    FLAGS = parser.parse_args()
+
+    model_name = "keras_mnist"
+    model_version = "1"
+
+    input_name = "dense_input"
+    shape = (1, 784)
+    datatype = 'FP32'
+
+    output_name = 'activation_2'
+
+    # Path of an image
+    image_path = '68747470733a2f2f646174616d61646e6573732e6769746875622e696f2f6173736574732f696d616765732f74665f66696c655f666565642f4d4e4953545f64696769742e706e67.png'
+
+    # The image is opened using Pillow, then converted to grayscale since the model deployed is trained on grayscale images
+    image = Image.open(image_path).convert('L')
+
+    # The image is resized to 28x28 pixels
+    image = image.resize(shape, Image.ANTIALIAS)
+
+    # The image is converted to a numpy array and data type is converted to float32 since the model is trained on float32
+    np_image = np.array(image).astype(np.float32)
+
+    # The image is reshaped to fit the model
+    np_image = np_image.reshape(-1, 784)
+
+    # Create an InferInput object with the input name, its data type and its shape defined
+    inferInput = InferInput(name=input_name, datatype=datatype, shape=shape)
+
+    # Set the data inside the InferInput object to the image in numpy format
+    inferInput.set_data_from_numpy(np_image)
+
+    # Create an InferenceServerClient and pass to it the url of the server
+    client = InferenceServerClient(url=FLAGS.url, verbose=FLAGS.verbose)
+
+    # Call client.infer(), pass the model name, version and the InferInput object inside a list since there can be multiple inputs
+    inferResult = client.infer(model_name=model_name, inputs=[inferInput], model_version=model_version)
+
+    # Print the output of the model in numpy format, pass in the name of the output layer
+    print(inferResult.as_numpy(output_name))